Patient monitoring system using wearable sensors with automated template matching

ABSTRACT

A wearable patient monitoring device in a patient monitoring system includes motion sensors generating sensor output signals in response to sensed patient motion, and a processor that processes the sensor output signals according to a template-matching monitoring process that includes (1) detecting occurrence of a first event of a multi-event movement based on first values of the sensor output signals, the multi-event movement having a finite-state-machine (FSM) representation as a sequence of states corresponding to events and expected values of the sensor output signals of the multi-event movement, (2) detecting occurrence of remaining events of the multi-event movement based on a sequence of subsequent values of the sensor output signals, and (3) upon detecting a last event of the multi-event movement, generating an output signal indicating detection of the patient performing the multi-event movement. Communications circuitry communicates the detection to a higher-level computerized device of the patient monitoring system.

BACKGROUND

The invention is related to the field of wearable sensors for patientmonitoring.

SUMMARY

Existing patient monitoring systems come in the form of bed pressuresensors, which warn medical staff when a patient's bed is unoccupied.However, there are problems with this approach. For example, if a guestcomes to visit the patient and the patient sits up from the bed, then analarm could go off. These unnecessary warnings contribute to alarmfatigue, the phenomenon of healthcare professionals ignoring constantlytriggering alarms. Another problem with these older systems is that theyare not wearable and hence will not work when the patient is not in thebed. They do not work, for example, when the patient is in the bathroom.Thus, there is a need for more specific activity detection that willonly alert when the specific movement is identified, and that allows apatient to move normally (i.e., to use the bathroom) while maintainingdesired monitoring.

Sensing technologies are currently used for many applications in modernmedicine. A study from UCLA created a Perfusion-Oxygenation Monitor (1).Analyzing perfusion and blood oxygenation is essential for treatingpatients with circulatory problems; treatments can also become costly ifthe patient is not diagnosed early. This study showed that continuouslymonitoring patients with wearable sensors is possible, and that it canrelay important information about the status of hospital patients to themedical staff.

On the detection side, a recent study reported on the usefulness of anepoch-based classifier for detecting a sit-stand transfer. Theepoch-based classifier associates certain patterns in the sensory datato certain motor movements. This study showed how the data of a subjectmoving from a sitting position to a standing position could be matchedwith a certain pattern, or template. The implementation of such aclassifier can be immensely helpful in deducing the state of a patient'sbody posture.

Wearable sensors designed for gait analysis have applications inrehabilitation and athletic training. These include gyroscopes,accelerometers, goniometers, magnetoresistive sensors andelectromyography sensors, and are placed at different locations on thebody. Another study on wearable sensor-based rehabilitation used threetri-axial accelerometers placed at different locations to relay angleinformation and identify the type of rehabilitation exercise. This studydeveloped an exercise assessment mechanism by allowing the sensors toguide patients through their own rehabilitation exercise. Wearabletechnologies are also used in connection with elite sports, including anInertial Movement Analysis (IMA) algorithm using gyroscopes andaccelerometers to measure force, direction and tilt. This algorithmhelps to track the movements of athletes who perform complex sequencesof movements including jumping, changing direction, and accelerating.Such movements are more difficult to track using GPS or camera systems.

Presently disclosed is a template-matching technique effective in thedetection of a specific target activity, such as getting out of bed(GOB), with greater sensitivity and specificity than existing methods.In one example a gyroscope sensor is used in combination with atemplate-based detector for patient monitoring. A data-driven approachcan be successful in detecting the target activity.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features and advantages will beapparent from the following description of particular embodiments of theinvention, as illustrated in the accompanying drawings in which likereference characters refer to the same parts throughout the differentviews.

FIG. 1 is a diagram depicting axes of motion of a patient;

FIG. 2 is a hardware block diagram of a patient monitoring system;

FIG. 3 is a plot of respective sensor output signals for aget-out-of-bed (GOB) movement;

FIG. 4 is a finite state machine (FSM) representation of a GOB movement;

FIG. 5 is a flow diagram of high-level operation.

DETAILED DESCRIPTION

The entire disclosure of U.S. provisional application 62/259,403 filedNov. 24, 2015 and entitled “Patient Monitoring System Using WearableSensors with Automated Template Matching” is incorporated by referenceherein.

FIG. 1 shows a patient 10 with a wearable patient monitoring device 12,which may be secured to the patient 10 by a belt or other means. FIG. 1shows three separate axes of movement of the patient 10, namely pitch(uprightness), roll (azimuthal orientation), and yaw (left/right lean).As described more below, the patient monitoring device 12 senses andanalyzes patient motion to detect certain complex movements. In oneimportant example, motion is analyzed for a so-called “get out of bed”or GOB movement, which can be useful in a clinical setting for alertingstaff when a patient has gotten out of bed. If the patient is a fallrisk or for other reasons should not be out of bed unattended, thedetection of a GOB movement by the device 12 can be used to alertclinical staff who can respond appropriately. More generally, thepatient monitoring device 12 uses a template approach to detectingpredefined complex movements for any of a variety of reasons.

FIG. 2 shows a patient monitoring system that incorporates the patientmonitoring device 12 along with a higher-level control (HLC) computer20. The patient monitoring device 12 includes a processor 22, agyroscope (GYRO) 24, a battery 26, and wireless communications circuitry(COMM) 28. In operation, the battery 26 supplies operating power to theother components as required. The gyroscope 24 senses three-axis motionand generates respective output signals that are provided to theprocessor 22. The communications circuitry 28 carries out wirelesscommunications with the HLC computer 20, using so-called “Wi-Fi” forexample. The processor 22 executes computer program instructions toreceive and analyze the signals from the gyroscope 24 in order to detectpatient movements, as described more below, and uses the communicationscircuitry 28 to communicate the detection of patient movements to theHLC computer 20.

The processor 22 may implement a data collection loop that polls thegyroscope 24 at a particular desired rate, e.g., 100 Hz, and may averageor otherwise filter the samples. In one example, the raw samples areaveraged over an interval of 50 samples, leaving a data representationof 2 Hz. The gyroscope 24 returns the rate of rotation for the threeaxes simultaneously, and the microprocessor-based program computes anintegral of the angular velocity with reference to an initial knownposition over a period of 10 seconds for each stream to give the angularbody positions in the three axes: pitch, yaw, and roll. At the end of asampling period, a sample average may be written to a serial line beforeresetting to zero. The software may also correct for zero-error using azero-threshold.

FIG. 3 shows an example of sets of samples of the gyroscope outputsignals over a suitable period, such as 20 seconds. Sample values are ina range shown as [−1000, 3500] (arbitrary units). In this example thedata points represents samples taken at 2 Hz, and the data points may beaveraged or filtered to reduce noise. Pitch, roll and yaw values areplotted at 30, 32 and 34 respectively. In the description below thesedimensions may also be referred to as x, y and z respectively.

FIG. 4 shows an FSM model for a GOB detection template. States arerepresented by circles, and transition events are represented by arrows.Operation based on this template is described more below.

Protocols and Data Acquisition

Epoch Detection

The disclosed technique employs time periods referred to as “epochs”during which there is sensed activity that may be declared as a detectedmovement (e.g., GOB movement). Epochs may be established by obtainingand analyzing sensor data in a training phase in which one or moresubjects perform the movement to be detected. In one example anepoch-detection method sweeps a pitch threshold ranging from 0 to 4000in steps of 500. The pitch and roll values from the sensor crosses thethreshold from lower to higher for greater than two seconds to detectthe activity/movement (e.g., GOB movement).

Template-Based Detection

As indicated, a training data set may be used to train the sensor systemto detect a certain movement. The training set protocol may include asequence of defined movements that a subject is made to perform, and thesequence may be repeated to capture normal variations. Training thesystem helps to prevent false negatives, i.e., instances in which thesystem fails to detect a true event. Training data may be collected byhaving the subject wear the monitoring device 12 and perform consecutivemovements, such as GOB movements each beginning in a supine position.

Data Processing and Template Matching Algorithm

Referring again briefly to FIG. 3, the pitch 30, roll 32, and yaw 34traces on the graph correspond to the pitch 30, roll 32, and yaw 34 ofthe monitoring device 12. Sensor data collected for the “Getting Out ofBed” scenario shows that the x-coordinate increases first, followed bythe y-coordinate and then a slight increase in the z-value. The initialincrease of x corresponds to the individual sitting up in bed; thefollowing increase of y corresponds to the individual turning to theside; and the final increase of z represents yaw from the individual'swalking strides. These separate signals can thus be mapped to a certainmovement pattern.

Each state in the activity detection process defines a specific epoch,or body configuration. The system analyzes the transitions betweenepochs in order to determine the activity being performed. Since thegyroscope 24 only measures angular rate of change, the signals areintegrated over a running window to smooth the data. Each epochcorresponds to a scenario such as GOB that can be viewed as a sequenceof movements. Hence, the system uses an FSM to identify the movementsequence. Referring to the example of FIG. 4, the FSM works like aflowchart that starts at State 0 and moves through the states viatransitions that correspond to movements such as pitch, roll, and yaw.The transitions are triggered based on the values of certain parameters,as explained more below.

FIG. 4 shows, for the specific example of a GOB movement, an FSM havingstates 0 through 6 and associated transitions. The parameters thatcontrol transitions include gob_st_th (min threshold for pitch),rel_xy_th (max ratio of the roll to the pitch) and sway parameter (minyaw). For example, when the subject pitches forward from an initialsupine position, gob_st_th is crossed, and the state moves from State 0to State 1 (transition shown as “Pitch+”). For the entire GOB movementperformed normally, the FSM moves through states 0, 1, 2, 3 and 4, whichcorrespond to Supine, Sit-up, Sit at edge, Leaning forward, and Standrespectively. If the subject were to get of bed abnormally, such asfalling before getting up, the FSM might move to State 5 (Floor) beforecoming to the final state. Thus, the FSM model allows for multiple setsof state transitions before the event is classified as a GOB event.

For the real-time FSM, the transitions correspond to the phases of thesensor data graph versus time. The states of the FSM capture differentkey points in the graphs of the multiple sensors. For example, thestates in FIG. 4 correspond to events such as: 1: “y has increased”; 2:“x has increased”; 3: “x has peaked but not y”; 4: “x and y have bothpeaked”; etc.

A template-matching method attempts to match data from a sensor to apattern, or template, which is representative of an activity.Epoch-detection is a form of template-matching where an activity ismodeled as a sequence of epochs that are then detected to identify theactivity. One way of identifying the epochs is through curve-fitting,and another way is using threshold crossings, which is computationallymore efficient than a curve fitting method. A method for epoch detectionthat is based on a Finite State Machine (FSM) identifies states in theshape of a curve that reflects the evolution of an activity.

Threshold-Based Epoch Detection

In one epoch detection method, two criteria are used: threshold andtimeframe. The results of an epoch-based detection study may be assessedusing a Receiver Operating Characteristic (ROC) curve. The epochdetection mechanism may first determine whether the pitch and the rollmotions of the GOB event both cross a certain threshold. An examplethreshold is 2000, which may be broadly applicable to many differentpatients/subjects. The second criterion is timeframe: in order for theevent to be classified as a GOB event, the motions in the GOB event haveto take a certain minimum time (e.g., at least two seconds) to complete.The timeframe criterion is implemented in order to validate that theevent taking place is a complete GOB event, and not just some sudden,non-GOB movement.

FSM-Based Epoch Detection

Another approach to epoch detection is a Finite State Machine(FSM)-based template-matching algorithm, an example of which is givenabove. The FSM detects an activity using a combination of state memorytogether with event triggers based on threshold crossings in identifyingthe movement. An advantage is that an FSM can distinguish multiplesequences of events that comprise the activity, improving thesensitivity of detection. Hence, it is more effective at detecting theGOB events than a pure threshold-based epoch detection and iscomputationally less intensive than a graph-matching epoch detection.

In one example, an FSM-based template matching algorithm can be assessedby calculating sensitivity and specificity of the system. Thesensitivity may be defined as TP/(TP+FN), and the specificity is definedas TN/(TN+FP), where a TP (True Positive) means both the sensor and anexpert outputted T; TN (True Negative) means both outputted F; FP (FalsePositive) means the sensor detected T while it was actually F; and FN(False Negative) means the sensor detected F while it was actually T.

An FSM-based template-matching algorithm can be effective in thedetection of a motor activity. A sensing algorithm as described hereinmay be more accurate in detecting events that are related to the normalGOB event. The system can be trained using a data particular to the GOBactivity, and may be tested against other (non-GOB) activities toevaluate its rate of false positives. In one example, sensitivity may bedetermined to be 5/(5+4)=0.56, and specificity 21/(21+5)=0.81, wheresensitivity is the proportion of actual GOB events that are detected andspecificity is the proportion of non-GOB events that are correctlyidentified as non-GOB. This may be evaluated by having a subject performnon-GOB activity such as household chores for a certain period (e.g.,one hour).

FIG. 5 is a high-level flow diagram of operation of the patientmonitoring device 12, specifically of the template-matching monitoringprocess implemented by the processor 22 with involvement of othercomponents.

At 50, the occurrence of a first event of a multi-event movement isdetected based on first values of the sensor output signals, where themulti-event movement has a finite-state-machine (FSM) representation asa sequence of states corresponding to respective events and associatedexpected values of the sensor output signals of the multi-eventmovement. In one example the multi-event movement is a GOB movement suchas described above, with the first event being a first transition ofbody configuration (e.g., pitching forward to the Sit-up position ofState 1).

At 52, there is subsequently detection of the occurrence of remainingevents of the multi-event movement based on a sequence of subsequentvalues of the sensor output signals matching the expected values of thesensor output signals in the FSM representation of the multi-eventmovement. In the above GOB example, these correspond to the transitionsto states 2, 3 and 4 for example.

At 54, upon detecting a last event of the multi-event movement, anoutput signal is generated indicating detection of the patientperforming the multi-event movement, and the detection of themulti-event movement is communicated to a higher-level computerizeddevice (e.g., HLC computer 20) of the patient monitoring system.

Applications

The wearable sensor system can have a beneficial impact on patient carein a hospital setting. Specifically, this system can be implemented forfall prevention of the elderly in a nursing home as well as convalescentpatients in post-operation recovery. The elderly may be susceptibletowards falls because they may lack the ambulatory skills and may bedisoriented, while convalescent patients may have impaired awareness orbalance due to the influence of drugs. Due to the large number ofpatients that doctors and nurses deal with on a day-to-day basis, theymay encounter multiple issues with the current system, including alarmfatigue and forgetting to turn the bed pressure sensor back on after ithas gone off. A continuous monitoring system with smart, algorithmicdetection is preferred in these situations.

A template-matching approach can be a way to improve the sensitivity ofGOB detection. The epoch-detection method requires having an a priorispecific knowledge of a reference pattern—that is difficult to knowbecause of the variation from one GOB event to another within the sameindividual and across individuals. Using a single threshold (such as2000, as mentioned above) causes a lower sensitivity; therefore, atemplate-matching technique was explored for improving sensitivity. Thetechnique uses an FSM, which tracks the states and transitions withinthe movement that corresponds to a particular motion template. Thisalgorithm uses noticeable patterns in sensory data to determinetransitions between different body configurations.

Study has demonstrated the efficacy of the FSM-based template matchingdetection as an improvement over epoch-based detection, which may have alower sensitivity because it is difficult to determine one thresholdthat accurately detects all the different GOB events. Also, theepoch-detection method based on graph matching is difficult to implementin real-time due to the high amount of processing required to scale thegraphs and match the patterns. In contrast, the FSM-based method hasonly minimal amount of state to be maintained, and yet can offer quickand accurate detection of the GOB scenario.

FSM-based detection can be effectively applied in detecting other typesof movements as well. This is because the FSM allows a complex movementto be broken down into sub-movements that are more easily detectable.One such activity that might benefit from such a detection method is thequick recovery using monitored rehabilitation treatment. For even morecomplex activities, an additional sensor such as an accelerometer mayhelp in providing other event-specifying information.

The FSM-based detection method is able to detect events in real-timeusing a modified FSM, where the transitions are defined based on thephases of the sensor data rather than any specific sub-movement. Usingthis approach, alerts can be relayed in real-time to an application onan attendant's cell phone or a monitoring station, which may bedesirable for fall prevention.

The description herein contains several specifics for compliance withdisclosure requirements. The invention is not necessarily limited to anyparticular implementation of its different aspects. The following aretwo specific areas of more general applicability of the invention:

-   -   1. The ability to detect motions other than getting out of bed        by employing the Finite State Machine (FSM) method for detection        -   an action can be viewed as a sequence of movements        -   the method can be used to detect complex movements in            sports, such as a jump shot in basketball, blocking or            tackling in football, or a soccer kick        -   the method can also be used in patient rehabilitation, to            keep track of a patient's range of motion—for example, it            can provide feedback to a patient in post-ACL reconstruction            surgery rehab so the user can gradually increase his/her            range of motion through feedback from the app for a faster            rehabilitation    -   2. Other alternatives        -   using a different type of microprocessor other than Arduino        -   using other sensors such as an accelerometer        -   using multiple sensors combined to assist the FSM        -   continuously monitoring and analyzing in real time to            determine trends in the progress of treatment

While various embodiments of the invention have been particularly shownand described, it will be understood by those skilled in the art thatvarious changes in form and details may be made therein withoutdeparting from the spirit of the invention as defined by the appendedclaims.

What is claimed is:
 1. A wearable patient monitoring device for use in apatient monitoring system, comprising: one or more motion sensorsgenerating respective sensor output signals in response to sensed motionof a patient wearing the patient monitoring device; and a processorcoupled to receive and process the sensor output signals according to atemplate-matching monitoring process including: detecting occurrence ofa first event of a multi-event movement based on first values of thesensor output signals, the multi-event movement having afinite-state-machine (FSM) representation as a sequence of statescorresponding to respective events and associated expected values of thesensor output signals of the multi-event movement; subsequentlydetecting occurrence of remaining events of the multi-event movementbased on a sequence of subsequent values of the sensor output signalsmatching the expected values of the sensor output signals in the FSMrepresentation of the multi-event movement; and upon detecting a lastevent of the multi-event movement, generating an output signalindicating detection of the patient performing the multi-event movement;and communications circuitry configured and operative in response to theoutput signal to communicate the detection of the patient performing themulti-event movement to a higher-level computerized device of thepatient monitoring system.
 2. The wearable patient monitoring device ofclaim 1, wherein the events are transitions between respectiveconfigurations of the patient's body, the configurations including (1)prone, (2) seated, and (3) standing.
 3. The wearable patient monitoringdevice of claim 2, wherein the transitions include prone-to-seated andseated-to-standing.
 4. The wearable patient monitoring device of claim1, wherein the motion sensors include rotational sensors for sensingrotation about one or more axes of the patient's body.
 5. The wearablepatient monitoring device of claim 4, wherein the rotational sensorssense rotation about a pitch axis, a roll axis, and a yaw axis of thepatient's body.
 6. The wearable patient monitoring device of claim 1,employing a modified FSM to detect events in real time, with transitionsbetween the states being defined based on phases of the sensor signalsrather than any specific sub-movement.
 7. The wearable patientmonitoring device of claim 6, wherein the communications circuitry isfurther configured and operative to relay alerts in real-time to anapplication on an attendant's device or a monitoring station.